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dc.contributor.authorBors, Christianen_US
dc.contributor.authorEichner, Christianen_US
dc.contributor.authorMiksch, Silviaen_US
dc.contributor.authorTominski, Christianen_US
dc.contributor.authorSchumann, Heidrunen_US
dc.contributor.authorGschwandtner, Theresiaen_US
dc.contributor.editorKerren, Andreas and Garth, Christoph and Marai, G. Elisabetaen_US
dc.description.abstractTime series segmentation is employed in various domains and continues to be a relevant topic of research. A segmentation pipeline is composed of different steps involving several parameterizable algorithms. Existing Visual Analytics approaches can help experts determine appropriate parameterizations and corresponding segmentation results for a given dataset. However, the results may also be afflicted with different types of uncertainties. Hence, experts face the additional challenge of understanding the reliability of multiple alternative the segmentation results. So far, the influence of uncertainties in the context of time series segmentation could not be investigated. We present an uncertainty-aware exploration approach for analyzing large sets of multivariate time series segmentations. The approach features an overview of uncertainties and time series segmentations, while detailed exploration is facilitated by (1) a lens-based focus+context technique and (2) uncertainty-based re-arrangement. The suitability of our uncertainty-aware design was evaluated in a quantitative user study, which resulted in interesting findings of general validity.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.subjectInformation systems
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.subjectVisualization techniques
dc.subjectComputing methodologies
dc.subjectVisual analytics
dc.titleExploring Time Series Segmentations Using Uncertainty and Focus+Context Techniquesen_US
dc.description.seriesinformationEuroVis 2020 - Short Papers
dc.description.sectionheadersAnalytics and Evaluation

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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License